Steven Piantadosi and Celeste Kidd to join BCS at Rochester

It’s great to be able to announce that the Center of Language Sciences at the University of Rochester is about to grow. Two new faculty, Steven Piantadosi and Celeste Kidd, will join our Brain and Cognitive Science department, starting in the Summer of 2014. Chances are you know both of them, but here’s a short intro.

Celeste’s research focuses on decision making, attention, and language development in infants and children. Her recent work includes research on the trade-off between too much and too little information/surprisal in learning and how infants seem to be striving for a middle ground (the ‘Goldilocks effect’). She also recently revisited the well-known marshmallow study, putting an intriguing new twist on it: her study suggests that kids can prioritize long-term over short-term rewards, if they have evidence that the long-term rewards will reliably be delivered (see the paper).

Steven’s research focuses on probabilistic inference to learn and process language. His thesis investigated probabilistic models of semantic acquisition — how complex thoughts are acquired through composition out of simpler thought elements (thesis). He has also authored several beautiful papers on how communicative pressures (formalized in terms of information theory and probabilistic inference) are cross-linguistically reflected in the phonological structure of the mental lexicon. Another line of his research focuses on recursion – see, for example, his recent work on recursion in Piraha (talk).

Together they won the 2010 Computational Modeling Prize (Perception/Action) of the Cognitive Science Society.

Knit from the command line

Knitr is a great way to combine document markup (Latex, Markdown, HTML, etc.) with R code for data analysis and visualization. It pulls out the chunks of R code, runs them, and re-inserts the results into the document source (usually a .tex file), which can then be compiled as usual. Normally you would call it from an R console (or use something like RStudio), but what if you want to call it from the command line, like latex?  Here’s a little shell script that I use to automate the knitting of .Rnw files (combining R and Latex): knit.sh.

It call knit() inside R, then runs pdflatex on the resulting file. It is very simple to use (you must of course have the knitr package installed in R):
knit.sh awesomefile.Rnw

This would produce awesomefile.pdf (as well as the intermediate file awesomefile.tex, and the extracted R commands, awesomefile.R). You might even rename the script as knit and put it somewhere on your search path (maybe /usr/local/bin/) to be even more fancy.

now, this is broader impact

This is federal funds well-spent: after the CUNY Sentence Processing Conference, Daniel Pontillo reaches out to the broader public and explains –to a captive audience of night owls at packed IHOP– how eye-tracking data allows us to test how we process the world (the poster is on implicit naming, or rather the lack thereof, in visual world experiments). The presentation was a resounding success. One member of an underrepresented minority was likely recruited for a research career in the cognitive sciences. A brawl that later ensued on the same premises stands in no relation to this presentation, in which only waffles were harmed. Science never stops. We are grateful for all feedback received from IHOPers during the poster presentation.

(Disclaimer: federal funds were only used to print the poster, which was first presented at the Sentence Processing Conference.)

Dan Pontillo gives an impromptu poster presentation at the IHOP around 2-something a.m., Columbia, S.C.

Academic minute about Masha’s work

An academic minute about Masha Fedzechkina’s work on the existence of a bias for efficient information transfer during language acquisition just came out — you can listen to it here.

The work described in the minute appeared as:

Fedzechkina, M., Jaeger, T.F. & Newport, E. (2012). Language learners restructure their input to facilitate efficient communication. Proceedings of the National Academy of Sciences, 109 (44): 17897—17902.
[paper (DOI)] [BibTeX]

Unfortunately, they did not mention the team members (contrary to what I was assured). The lead author, Masha Fedzechkina, is a fourth year graduate student in the Brain and Cognitive Sciences at the University of Rochester. The work was jointly advised by Elissa Newport (the director of the new Center for Brain Plasticity and Recovery at Georgetown University) and me.

Special Issue on “Laboratory in the Field: Advances in cross-linguistic psycholinguistics”

Aim

We invite original and unpublished papers on psycholinguistic research on lesser-studied languages, for a special issue of Language and Cognitive Processes. Our purpose is to bring together researchers who are currently engaged in empirical research on language processing in typologically diverse languages, in order to establish the emerging field of cross-linguistic psycholinguistics as a cross-disciplinary research program. Both submissions that extend the empirical coverage of psycholinguistic theories (e.g., test whether supposedly universal processing mechanisms hold cross-linguistically) and submissions that revise and extend psycholinguistic and linguistic theory through quantitative data are welcome. The special issue will focus on the architecture and mechanisms underlying language processing (both comprehension and production) at the lexical and sentence level. This includes studies on phonological and morphological processing to the extent that they speak to the organization, representation, and processing of lexical units or the interaction of these processes with sentence processing. We seek behavioral, neurocognitive (e.g., ERP, fMRI), and quantitative corpus studies in any of these areas.

Effects of phonological overlap on fluency, speech rate, and word order in unscripted sentence production

The last two papers based on Katrina Furth’s and Caitie Hilliard’s work back when they were at Rochester just came out in the Journal of Experimental Psychology: Learning, Memory, and Cognition and the journal Frontiers in Psychology.

The JEP:LMC paper investigates how lemma selection (i.e., word choice) is affected by phonological overlap. We find evidence for a (weak) bias against sequences of phonologically onset overlapping words. That is, when speakers have a choice, they seem to prefer sentences like “Hannah gave the hammer to the boy”, rather than “Hannah handed the hammer to the boy”. This suggests very early effects of phonology on lexical production, which seem to be incompatible with strictly serial models of word production.

Jaeger, T. F., Furth, K., and Hilliard, C. 2012. Phonological overlap affects lexical selection during sentence production. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(5), 1439-1449. [doi: 10.1037/a0027862]

The Frontiers paper investigates

Language is shaped by brain’s desire for clarity and ease

Congratulations to Masha Fedzechkina on her article on a bias for efficient information transfer during language learning that has just appeared in the Proceedings of the National Academy of Sciences (link to article).

Here’s some news coverage

• Thanks to the extended podcast coverage by the Skeptic’s Guide to the Universe, which present an interesting take on our study (a couple of details about ambiguity vs. uncertainty are a bit off, but the general message is captured well). Have a look at minutes 13:30 to 22:02. Thanks to Nick Kloehn for making us aware of this piece.
• www.eurekalert.org (e.g., here).
• Check out an extended article on ScienceOmega (Language learning balances clarity and effort)
• Universities at which the research was conducted:
  • University of Rochester (Language is shaped by brain’s desire for clarity and ease)
  • Georgetown University (PNAS Study: Language Structure Arises from Balance of Clear and Effective Communication).
• Blogs and more: Futurity, Phys.org, ScienceBlog, Science Daily, E-Science News , Sify, TruthDive , BioSpace

More to come soon.

Errata: We are sorry that in our paper we forgot to acknowledge the help of three undergraduate research assistants, Andy Wood, Irene Minkina, and Cassandra Donatelli, in preparing the video animations used during our artificial language learning task.